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<table width="100%" summary="page for deguelin"><tr><td>deguelin</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Deguelin applied to chrysanthemum aphis</h2>

<h3>Description</h3>

<p>Quantal assay data from an experiment where the insectide deguelin was applied to
<em>Macrosiphoniella sanborni</em>.
</p>


<h3>Usage</h3>

<pre>data(deguelin)</pre>


<h3>Format</h3>

<p>A data frame with 6 observations on the following 4 variables.
</p>

<dl>
<dt><code>dose</code></dt><dd><p>a numeric vector of doses applied</p>
</dd>
<dt><code>log10dose</code></dt><dd><p>a numeric vector of logarithm-transformed doses</p>
</dd>
<dt><code>r</code></dt><dd><p>a numeric vector contained number of dead insects</p>
</dd>
<dt><code>n</code></dt><dd><p>a numeric vector contained the total number of insects</p>
</dd>
</dl>



<h3>Details</h3>

<p>The log-logistic model provides an inadequate fit.
</p>
<p>The dataset is used in Nottingham and Birch (2000) to illustrate a semiparametric approach to dose-response
modelling.
</p>


<h3>Source</h3>

 
<p>Morgan, B. J. T. (1992) <em>Analysis of Quantal Response Data</em>, London: Chapman \&amp; Hall/CRC (Table 3.9, p. 117).
</p>


<h3>References</h3>

<p>Notttingham, Q. J. and Birch, J. B. (2000) A semiparametric approach to analysing dose-response data, <em>Statist. Med.</em>, <b>19</b>, 389&ndash;404.
</p>


<h3>Examples</h3>

<pre>

## Log-logistic fit
deguelin.m1 &lt;- drm(r/n~dose, weights=n, data=deguelin, fct=LL.2(), type="binomial")
modelFit(deguelin.m1)
summary(deguelin.m1)

## Loess fit
deguelin.m2 &lt;- loess(r/n~dose, data=deguelin, degree=1)

## Plot of data with fits superimposed
plot(deguelin.m1, ylim=c(0.2,1))
lines(1:60, predict(deguelin.m2, newdata=data.frame(dose=1:60)), col = 2, lty = 2)

lines(1:60, 0.95*predict(deguelin.m2, 
newdata=data.frame(dose=1:60))+0.05*predict(deguelin.m1, newdata=data.frame(dose=1:60), se = FALSE),
col = 3, lty=3)

</pre>


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